ArticleJournal of the American Medical Informatics Association : JAMIA
Drug-drug interaction through molecular structure similarity analysis.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers.
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Who cites it
90 citing papers in PubMed.
- Robust prediction of drug combination side effects in realistic settings.PLoS computational biology · 2026Article
- How Advanced Artificial Intelligence Technologies Shape Drug-Drug and Drug-Target Interaction Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- MHAFR-DDI: a multimodal hierarchical attention fusion and relation-aware architecture for drug-drug interaction event prediction.Briefings in bioinformatics · 2026Article
- Machine Learning-Driven Bioprospecting of Cholinergic Medicinal Plants for Putative Leads Against Butyrylcholinesterase Towards Alzheimer's Care.Biochemistry research international · 2026Article
- FARFOOD: a database of potential interactions between food compounds and drugs.BioData mining · 2025Article
- MDG-DDI: multi-feature drug graph for drug-drug interaction prediction.BMC bioinformatics · 2025Article
- RCAN-DDI: Relation-aware cross adversarial network for drug-drug interaction prediction.Journal of pharmaceutical analysis · 2025Article
- DDI-CYP: Metabolism ensemble models for drug-drug interaction predictions.Drug metabolism and disposition: the biological fate of chemicals · 2025Article
- Are We Considering All the Potential Drug-Drug Interactions in Women's Reproductive Health? A Predictive Model Approach.Pharmaceutics · 2025Article
- A Multi-View Feature-Based Interpretable Deep Learning Framework for Drug-Drug Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2025Article
- A cohomology-based Gromov-Hausdorff metric approach for quantifying molecular similarity.Scientific reports · 2025Article
- A Fusion Deep Learning Model for Predicting Adverse Drug Reactions Based on Multiple Drug Characteristics.Life (Basel, Switzerland) · 2025Article
- Interaction between post-tumor inflammation and vascular smooth muscle cell dysfunction in sepsis-induced cardiomyopathy.Frontiers in immunology · 2025Article
- Machine learning-based drug-drug interaction prediction: a critical review of models, limitations, and data challenges.Frontiers in pharmacology · 2025Review
- Predicting the toxic side effects of drug interactions using chemical structures and protein sequences.Scientific reports · 2024Article
- PTB-DDI: An Accurate and Simple Framework for Drug-Drug Interaction Prediction Based on Pre-Trained Tokenizer and BiLSTM Model.International journal of molecular sciences · 2024Article
- A substructure-aware graph neural network incorporating relation features for drug-drug interaction prediction.Quantitative biology (Beijing, China) · 2024Article
- DRGATAN: Directed relation graph attention aware network for asymmetric drug-drug interaction prediction.iScience · 2024Article
- DDI-Transform: A neural network for predicting drug-drug interaction events.Quantitative biology (Beijing, China) · 2024Article
- DeepARV: ensemble deep learning to predict drug-drug interaction of clinical relevance with antiretroviral therapy.NPJ systems biology and applications · 2024Article
30 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundDrug-drug interactions (DDIs) are responsible for many serious adverse events; their detection is crucial for patient safety but is very challenging. Currently, the US Food and Drug Administration and pharmaceutical companies are showing great interest in the development of improved tools for identifying DDIs.
methodsWe present a new methodology applicable on a large scale that identifies novel DDIs based on molecular structural similarity to drugs involved in established DDIs. The underlying assumption is that if drug A and drug B interact to produce a specific biological effect, then drugs similar to drug A (or drug B) are likely to interact with drug B (or drug A) to produce the same effect. DrugBank was used as a resource for collecting 9454 established DDIs. The structural similarity of all pairs of drugs in DrugBank was computed to identify DDI candidates.
resultsThe methodology was evaluated using as a gold standard the interactions retrieved from the initial DrugBank database. Results demonstrated an overall sensitivity of 0.68, specificity of 0.96, and precision of 0.26. Additionally, the methodology was also evaluated in an independent test using the Micromedex/Drugdex database.
conclusionThe proposed methodology is simple, efficient, allows the investigation of large numbers of drugs, and helps highlight the etiology of DDI. A database of 58 403 predicted DDIs with structural evidence is provided as an open resource for investigators seeking to analyze DDIs.
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